Segmenting Motion Capture Data into Distinct Behaviors Graphics Interface ‘ 04 Speaker: Alvin January 17, 2005

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 2 Outline Introduction Related Work PCA PPCA GMM Results Conclusions

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 3 Introduction Motion data are segmented at capture or by hand and are often small clips. Longer shots contain natural transitions. Segment motion into high-level behaviors. Unsupervised Learning Focus on efficient techniques: PCA, PPCA and GMM.

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 4 Related Work Model-based Approach Low-level Detect zero crossings of angular velocities. Motion texton State Machine or Motion Graph High-level HMM Clustering

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 5 Goal Input: Motion data (14 motions, each 8000 frames) FPS= Joints Specify the rotation relative to the parent for all joints. Rotations are specified by quaternions. Output: Motion Clips Automatically Distinct Behaviors Longer

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 6 Center of motion: Approximation: SVD: Dimension: Projection Error: Derivative: PCA

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 7 PCA Cut if d i more than 3 standard deviations from the average

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 8 Probabilistic PCA Average square of discard singular values: Covariance Matrix: Average Mahalanobis Distance T=150, K=T K:=K+ △, △ =10, Threshold R=15

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 9 PPCA

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 10 Gaussian Mixture Model Pre-processing: Use PCA to project onto lower dimensional subspace. (Speed up EM) Preserve 90% of the variance. Each cluster is represented by a Gaussian Distribution. EM Estimate mean, covariance matrix, prior

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 11 GMM

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 12 GMM Cut if frames x i and x i+1 belong to different clusters

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 13 Results Error Matrix for PCA Error Matrix for PPCA

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 14 Results

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 15 Results Precision: Reported correct cuts / The total number of reported cuts Recall : Reported correct cuts / The total number of correct cuts

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 16 Evaluation Form 論文簡報部份 完整性介紹 (4) 系統性介紹 (4) 表達能力 (3) 投影片製作 (3) 論文審閱部分 瞭解論文內容 (4) 結果正確性與完整性 (4) 原創性與重要性 (4) 讀後啟發與應用： The mahalanobis distance can be adopted to my classification of motions. Besides, maybe I can exploit the GMM technique to classify for comparison.

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 17 Conclusions Imperfect because observations ’ opinions. Treat all weights of DOF equally. Each method require some parameters. PCA-based methods work well. ICA may achieve better cut detection. No segmentation will apply for all applications.

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 18 Mahalanobis Distance D t (x) = (x – m t )S -1 t (x – m t )' D t is the distance from t group S t represents the within-group covariance matrix m t is the vector of the means of t group X is the vector of frame values at location x Superior to Euclidean distance because it takes distribution of the points (correlations) into account Useful to determine the ” similarity ” from an unknown sample to known samples Classify observations into different groups

Alvin/GAME Lab./CSIE/NDHU Segmenting Motion Capture Data into Distinct Behaviors 19 GMM by Using EM